Overview

Dataset statistics

Number of variables31
Number of observations579958
Missing cells2981327
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.2 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (94.9%)Imbalance
DIVERTED is highly imbalanced (97.8%)Imbalance
CANCELLATION_CODE has 576648 (99.4%) missing valuesMissing
CARRIER_DELAY has 475839 (82.0%) missing valuesMissing
WEATHER_DELAY has 475839 (82.0%) missing valuesMissing
NAS_DELAY has 475839 (82.0%) missing valuesMissing
SECURITY_DELAY has 475839 (82.0%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 475839 (82.0%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 22.11241594)Skewed
SECURITY_DELAY is highly skewed (γ1 = 151.3139368)Skewed
DEP_DELAY has 28184 (4.9%) zerosZeros
ARR_DELAY has 10858 (1.9%) zerosZeros
CARRIER_DELAY has 41858 (7.2%) zerosZeros
WEATHER_DELAY has 99571 (17.2%) zerosZeros
NAS_DELAY has 57455 (9.9%) zerosZeros
SECURITY_DELAY has 103517 (17.8%) zerosZeros
LATE_AIRCRAFT_DELAY has 50580 (8.7%) zerosZeros

Reproduction

Analysis started2024-03-30 05:49:34.144341
Analysis finished2024-03-30 05:52:27.154448
Duration2 minutes and 53.01 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7665296
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:27.252748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0019356
Coefficient of variation (CV)0.53150666
Kurtosis-1.2111724
Mean3.7665296
Median Absolute Deviation (MAD)2
Skewness0.17139827
Sum2184429
Variance4.0077461
MonotonicityIncreasing
2024-03-30T02:52:27.513989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 97754
16.9%
3 93480
16.1%
2 91668
15.8%
5 78217
13.5%
4 78087
13.5%
7 74954
12.9%
6 65798
11.3%
ValueCountFrequency (%)
1 97754
16.9%
2 91668
15.8%
3 93480
16.1%
4 78087
13.5%
5 78217
13.5%
6 65798
11.3%
7 74954
12.9%
ValueCountFrequency (%)
7 74954
12.9%
6 65798
11.3%
5 78217
13.5%
4 78087
13.5%
3 93480
16.1%
2 91668
15.8%
1 97754
16.9%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
Minimum2023-05-01 00:00:00
Maximum2023-05-31 00:00:00
2024-03-30T02:52:27.959572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:28.440736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
WN
122521 
DL
84459 
AA
79782 
UA
62340 
OO
56397 
Other values (10)
174459 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1159916
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 122521
21.1%
DL 84459
14.6%
AA 79782
13.8%
UA 62340
10.7%
OO 56397
9.7%
YX 26540
 
4.6%
B6 24639
 
4.2%
NK 22506
 
3.9%
AS 20641
 
3.6%
MQ 17641
 
3.0%
Other values (5) 62492
10.8%

Length

2024-03-30T02:52:28.716541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 122521
21.1%
dl 84459
14.6%
aa 79782
13.8%
ua 62340
10.7%
oo 56397
9.7%
yx 26540
 
4.6%
b6 24639
 
4.2%
nk 22506
 
3.9%
as 20641
 
3.6%
mq 17641
 
3.0%
Other values (5) 62492
10.8%

Most occurring characters

ValueCountFrequency (%)
A 249439
21.5%
N 145027
12.5%
O 128843
11.1%
W 122521
10.6%
D 84459
 
7.3%
L 84459
 
7.3%
U 62340
 
5.4%
9 30127
 
2.6%
Y 26540
 
2.3%
X 26540
 
2.3%
Other values (11) 199621
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1159916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 249439
21.5%
N 145027
12.5%
O 128843
11.1%
W 122521
10.6%
D 84459
 
7.3%
L 84459
 
7.3%
U 62340
 
5.4%
9 30127
 
2.6%
Y 26540
 
2.3%
X 26540
 
2.3%
Other values (11) 199621
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1159916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 249439
21.5%
N 145027
12.5%
O 128843
11.1%
W 122521
10.6%
D 84459
 
7.3%
L 84459
 
7.3%
U 62340
 
5.4%
9 30127
 
2.6%
Y 26540
 
2.3%
X 26540
 
2.3%
Other values (11) 199621
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1159916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 249439
21.5%
N 145027
12.5%
O 128843
11.1%
W 122521
10.6%
D 84459
 
7.3%
L 84459
 
7.3%
U 62340
 
5.4%
9 30127
 
2.6%
Y 26540
 
2.3%
X 26540
 
2.3%
Other values (11) 199621
17.2%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5906
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2276.8748
Minimum1
Maximum8815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:29.136219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile286
Q11044
median2036
Q33289
95-th percentile5345
Maximum8815
Range8814
Interquartile range (IQR)2245

Descriptive statistics

Standard deviation1549.9056
Coefficient of variation (CV)0.6807162
Kurtosis-0.51495536
Mean2276.8748
Median Absolute Deviation (MAD)1062
Skewness0.63462089
Sum1.3204918 × 109
Variance2402207.2
MonotonicityNot monotonic
2024-03-30T02:52:29.448214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
538 385
 
0.1%
533 343
 
0.1%
777 335
 
0.1%
1073 332
 
0.1%
341 314
 
0.1%
354 307
 
0.1%
374 301
 
0.1%
2095 296
 
0.1%
1355 291
 
0.1%
201 289
 
< 0.1%
Other values (5896) 576765
99.4%
ValueCountFrequency (%)
1 142
< 0.1%
2 176
< 0.1%
3 130
< 0.1%
4 198
< 0.1%
5 90
< 0.1%
6 87
< 0.1%
7 95
< 0.1%
8 70
 
< 0.1%
9 179
< 0.1%
10 196
< 0.1%
ValueCountFrequency (%)
8815 4
< 0.1%
8811 1
 
< 0.1%
8801 2
< 0.1%
8800 4
< 0.1%
8799 1
 
< 0.1%
8790 1
 
< 0.1%
8789 1
 
< 0.1%
8788 2
< 0.1%
8787 2
< 0.1%
8786 2
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct342
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12651.597
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:29.727619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1527.9889
Coefficient of variation (CV)0.12077439
Kurtosis-1.2972769
Mean12651.597
Median Absolute Deviation (MAD)1591
Skewness0.10392936
Sum7.337395 × 109
Variance2334750.1
MonotonicityNot monotonic
2024-03-30T02:52:30.111020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28740
 
5.0%
11292 23997
 
4.1%
11298 23900
 
4.1%
13930 21932
 
3.8%
12892 16736
 
2.9%
12889 16205
 
2.8%
11057 16121
 
2.8%
14107 14621
 
2.5%
12953 14517
 
2.5%
13204 13983
 
2.4%
Other values (332) 389206
67.1%
ValueCountFrequency (%)
10135 351
 
0.1%
10136 93
 
< 0.1%
10140 2039
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 100
 
< 0.1%
10155 91
 
< 0.1%
10157 142
 
< 0.1%
10158 229
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 141
 
< 0.1%
16218 124
 
< 0.1%
15991 62
 
< 0.1%
15919 980
0.2%
15897 29
 
< 0.1%
15841 62
 
< 0.1%
15624 837
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

ORIGIN
Text

Distinct342
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:30.808959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1739874
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROC
2nd rowITH
3rd rowCLE
4th rowIAD
5th rowJFK
ValueCountFrequency (%)
atl 28740
 
5.0%
den 23997
 
4.1%
dfw 23900
 
4.1%
ord 21932
 
3.8%
lax 16736
 
2.9%
las 16205
 
2.8%
clt 16121
 
2.8%
phx 14621
 
2.5%
lga 14517
 
2.5%
mco 13983
 
2.4%
Other values (332) 389206
67.1%
2024-03-30T02:52:31.722733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 199031
 
11.4%
L 159705
 
9.2%
S 149520
 
8.6%
D 136291
 
7.8%
T 91277
 
5.2%
O 89843
 
5.2%
C 87132
 
5.0%
M 77789
 
4.5%
F 72666
 
4.2%
W 68452
 
3.9%
Other values (16) 608168
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 199031
 
11.4%
L 159705
 
9.2%
S 149520
 
8.6%
D 136291
 
7.8%
T 91277
 
5.2%
O 89843
 
5.2%
C 87132
 
5.0%
M 77789
 
4.5%
F 72666
 
4.2%
W 68452
 
3.9%
Other values (16) 608168
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 199031
 
11.4%
L 159705
 
9.2%
S 149520
 
8.6%
D 136291
 
7.8%
T 91277
 
5.2%
O 89843
 
5.2%
C 87132
 
5.0%
M 77789
 
4.5%
F 72666
 
4.2%
W 68452
 
3.9%
Other values (16) 608168
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 199031
 
11.4%
L 159705
 
9.2%
S 149520
 
8.6%
D 136291
 
7.8%
T 91277
 
5.2%
O 89843
 
5.2%
C 87132
 
5.0%
M 77789
 
4.5%
F 72666
 
4.2%
W 68452
 
3.9%
Other values (16) 608168
35.0%
Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:32.252630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.034909
Min length8

Characters and Unicode

Total characters7559700
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRochester, NY
2nd rowIthaca/Cortland, NY
3rd rowCleveland, OH
4th rowWashington, DC
5th rowNew York, NY
ValueCountFrequency (%)
ca 63479
 
4.7%
tx 60293
 
4.5%
fl 51214
 
3.8%
ny 33161
 
2.5%
san 30927
 
2.3%
ga 30911
 
2.3%
new 30880
 
2.3%
il 30381
 
2.2%
chicago 29268
 
2.2%
atlanta 28740
 
2.1%
Other values (408) 963452
71.2%
2024-03-30T02:52:33.023913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
772748
 
10.2%
, 579958
 
7.7%
a 577216
 
7.6%
o 417203
 
5.5%
e 398416
 
5.3%
n 371503
 
4.9%
t 359175
 
4.8%
l 331223
 
4.4%
i 285708
 
3.8%
r 274691
 
3.6%
Other values (47) 3191859
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7559700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
772748
 
10.2%
, 579958
 
7.7%
a 577216
 
7.6%
o 417203
 
5.5%
e 398416
 
5.3%
n 371503
 
4.9%
t 359175
 
4.8%
l 331223
 
4.4%
i 285708
 
3.8%
r 274691
 
3.6%
Other values (47) 3191859
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7559700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
772748
 
10.2%
, 579958
 
7.7%
a 577216
 
7.6%
o 417203
 
5.5%
e 398416
 
5.3%
n 371503
 
4.9%
t 359175
 
4.8%
l 331223
 
4.4%
i 285708
 
3.8%
r 274691
 
3.6%
Other values (47) 3191859
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7559700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
772748
 
10.2%
, 579958
 
7.7%
a 577216
 
7.6%
o 417203
 
5.5%
e 398416
 
5.3%
n 371503
 
4.9%
t 359175
 
4.8%
l 331223
 
4.4%
i 285708
 
3.8%
r 274691
 
3.6%
Other values (47) 3191859
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:33.405369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1701554
Min length4

Characters and Unicode

Total characters4738347
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowOhio
4th rowVirginia
5th rowNew York
ValueCountFrequency (%)
california 63479
 
9.5%
texas 60293
 
9.0%
florida 51214
 
7.7%
new 48776
 
7.3%
york 33161
 
5.0%
georgia 30911
 
4.6%
illinois 30381
 
4.6%
carolina 29367
 
4.4%
colorado 26056
 
3.9%
north 25213
 
3.8%
Other values (51) 267897
40.2%
2024-03-30T02:52:34.047095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 633376
13.4%
i 533340
 
11.3%
o 451285
 
9.5%
n 347472
 
7.3%
r 344588
 
7.3%
e 294096
 
6.2%
s 270808
 
5.7%
l 261741
 
5.5%
C 120764
 
2.5%
d 113454
 
2.4%
Other values (37) 1367423
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4738347
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 633376
13.4%
i 533340
 
11.3%
o 451285
 
9.5%
n 347472
 
7.3%
r 344588
 
7.3%
e 294096
 
6.2%
s 270808
 
5.7%
l 261741
 
5.5%
C 120764
 
2.5%
d 113454
 
2.4%
Other values (37) 1367423
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4738347
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 633376
13.4%
i 533340
 
11.3%
o 451285
 
9.5%
n 347472
 
7.3%
r 344588
 
7.3%
e 294096
 
6.2%
s 270808
 
5.7%
l 261741
 
5.5%
C 120764
 
2.5%
d 113454
 
2.4%
Other values (37) 1367423
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4738347
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 633376
13.4%
i 533340
 
11.3%
o 451285
 
9.5%
n 347472
 
7.3%
r 344588
 
7.3%
e 294096
 
6.2%
s 270808
 
5.7%
l 261741
 
5.5%
C 120764
 
2.5%
d 113454
 
2.4%
Other values (37) 1367423
28.9%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.306536
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:34.407363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.86296
Coefficient of variation (CV)0.49465427
Kurtosis-1.3219643
Mean54.306536
Median Absolute Deviation (MAD)22
Skewness-0.013525889
Sum31495510
Variance721.61862
MonotonicityNot monotonic
2024-03-30T02:52:34.722101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 63479
 
10.9%
74 60293
 
10.4%
33 51214
 
8.8%
22 33161
 
5.7%
34 30911
 
5.3%
41 30381
 
5.2%
82 26056
 
4.5%
36 23857
 
4.1%
38 20414
 
3.5%
85 17797
 
3.1%
Other values (42) 222395
38.3%
ValueCountFrequency (%)
1 3044
 
0.5%
2 11251
1.9%
3 3306
 
0.6%
4 449
 
0.1%
5 105
 
< 0.1%
11 1862
 
0.3%
12 1238
 
0.2%
13 12744
2.2%
14 574
 
0.1%
15 1285
 
0.2%
ValueCountFrequency (%)
93 15970
 
2.8%
92 6670
 
1.2%
91 63479
10.9%
88 577
 
0.1%
87 9607
 
1.7%
86 2259
 
0.4%
85 17797
 
3.1%
84 1898
 
0.3%
83 2249
 
0.4%
82 26056
4.5%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct342
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12651.522
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:35.027478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1528
Coefficient of variation (CV)0.12077598
Kurtosis-1.2973229
Mean12651.522
Median Absolute Deviation (MAD)1591
Skewness0.10400847
Sum7.3373511 × 109
Variance2334783.9
MonotonicityNot monotonic
2024-03-30T02:52:35.318543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28741
 
5.0%
11292 23988
 
4.1%
11298 23926
 
4.1%
13930 21941
 
3.8%
12892 16734
 
2.9%
12889 16196
 
2.8%
11057 16141
 
2.8%
14107 14620
 
2.5%
12953 14514
 
2.5%
13204 13981
 
2.4%
Other values (332) 389176
67.1%
ValueCountFrequency (%)
10135 351
 
0.1%
10136 93
 
< 0.1%
10140 2038
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 100
 
< 0.1%
10155 91
 
< 0.1%
10157 142
 
< 0.1%
10158 229
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 141
 
< 0.1%
16218 124
 
< 0.1%
15991 62
 
< 0.1%
15919 982
0.2%
15897 29
 
< 0.1%
15841 62
 
< 0.1%
15624 837
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

DEST
Text

Distinct342
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:35.961855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1739874
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowJFK
3rd rowJFK
4th rowLGA
5th rowCLE
ValueCountFrequency (%)
atl 28741
 
5.0%
den 23988
 
4.1%
dfw 23926
 
4.1%
ord 21941
 
3.8%
lax 16734
 
2.9%
las 16196
 
2.8%
clt 16141
 
2.8%
phx 14620
 
2.5%
lga 14514
 
2.5%
mco 13981
 
2.4%
Other values (332) 389176
67.1%
2024-03-30T02:52:36.959744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 198994
 
11.4%
L 159700
 
9.2%
S 149511
 
8.6%
D 136309
 
7.8%
T 91292
 
5.2%
O 89854
 
5.2%
C 87153
 
5.0%
M 77779
 
4.5%
F 72687
 
4.2%
W 68468
 
3.9%
Other values (16) 608127
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 198994
 
11.4%
L 159700
 
9.2%
S 149511
 
8.6%
D 136309
 
7.8%
T 91292
 
5.2%
O 89854
 
5.2%
C 87153
 
5.0%
M 77779
 
4.5%
F 72687
 
4.2%
W 68468
 
3.9%
Other values (16) 608127
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 198994
 
11.4%
L 159700
 
9.2%
S 149511
 
8.6%
D 136309
 
7.8%
T 91292
 
5.2%
O 89854
 
5.2%
C 87153
 
5.0%
M 77779
 
4.5%
F 72687
 
4.2%
W 68468
 
3.9%
Other values (16) 608127
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 198994
 
11.4%
L 159700
 
9.2%
S 149511
 
8.6%
D 136309
 
7.8%
T 91292
 
5.2%
O 89854
 
5.2%
C 87153
 
5.0%
M 77779
 
4.5%
F 72687
 
4.2%
W 68468
 
3.9%
Other values (16) 608127
35.0%
Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:37.425527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.035001
Min length8

Characters and Unicode

Total characters7559753
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowNew York, NY
4th rowNew York, NY
5th rowCleveland, OH
ValueCountFrequency (%)
ca 63472
 
4.7%
tx 60319
 
4.5%
fl 51187
 
3.8%
ny 33161
 
2.5%
san 30930
 
2.3%
ga 30908
 
2.3%
new 30879
 
2.3%
il 30388
 
2.2%
chicago 29275
 
2.2%
atlanta 28741
 
2.1%
Other values (408) 963431
71.2%
2024-03-30T02:52:38.211852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
772733
 
10.2%
, 579958
 
7.7%
a 577214
 
7.6%
o 417264
 
5.5%
e 398338
 
5.3%
n 371504
 
4.9%
t 359253
 
4.8%
l 331260
 
4.4%
i 285723
 
3.8%
r 274704
 
3.6%
Other values (47) 3191802
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7559753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
772733
 
10.2%
, 579958
 
7.7%
a 577214
 
7.6%
o 417264
 
5.5%
e 398338
 
5.3%
n 371504
 
4.9%
t 359253
 
4.8%
l 331260
 
4.4%
i 285723
 
3.8%
r 274704
 
3.6%
Other values (47) 3191802
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7559753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
772733
 
10.2%
, 579958
 
7.7%
a 577214
 
7.6%
o 417264
 
5.5%
e 398338
 
5.3%
n 371504
 
4.9%
t 359253
 
4.8%
l 331260
 
4.4%
i 285723
 
3.8%
r 274704
 
3.6%
Other values (47) 3191802
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7559753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
772733
 
10.2%
, 579958
 
7.7%
a 577214
 
7.6%
o 417264
 
5.5%
e 398338
 
5.3%
n 371504
 
4.9%
t 359253
 
4.8%
l 331260
 
4.4%
i 285723
 
3.8%
r 274704
 
3.6%
Other values (47) 3191802
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:38.595493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1702606
Min length4

Characters and Unicode

Total characters4738408
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowOhio
ValueCountFrequency (%)
california 63472
 
9.5%
texas 60319
 
9.0%
florida 51187
 
7.7%
new 48773
 
7.3%
york 33161
 
5.0%
georgia 30908
 
4.6%
illinois 30388
 
4.6%
carolina 29379
 
4.4%
colorado 26047
 
3.9%
north 25231
 
3.8%
Other values (51) 267898
40.2%
2024-03-30T02:52:39.217269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 633361
13.4%
i 533332
 
11.3%
o 451253
 
9.5%
n 347474
 
7.3%
r 344591
 
7.3%
e 294107
 
6.2%
s 270873
 
5.7%
l 261733
 
5.5%
C 120756
 
2.5%
d 113409
 
2.4%
Other values (37) 1367519
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4738408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 633361
13.4%
i 533332
 
11.3%
o 451253
 
9.5%
n 347474
 
7.3%
r 344591
 
7.3%
e 294107
 
6.2%
s 270873
 
5.7%
l 261733
 
5.5%
C 120756
 
2.5%
d 113409
 
2.4%
Other values (37) 1367519
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4738408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 633361
13.4%
i 533332
 
11.3%
o 451253
 
9.5%
n 347474
 
7.3%
r 344591
 
7.3%
e 294107
 
6.2%
s 270873
 
5.7%
l 261733
 
5.5%
C 120756
 
2.5%
d 113409
 
2.4%
Other values (37) 1367519
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4738408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 633361
13.4%
i 533332
 
11.3%
o 451253
 
9.5%
n 347474
 
7.3%
r 344591
 
7.3%
e 294107
 
6.2%
s 270873
 
5.7%
l 261733
 
5.5%
C 120756
 
2.5%
d 113409
 
2.4%
Other values (37) 1367519
28.9%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.304236
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:39.563566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.863506
Coefficient of variation (CV)0.49468527
Kurtosis-1.321858
Mean54.304236
Median Absolute Deviation (MAD)22
Skewness-0.013531847
Sum31494176
Variance721.64793
MonotonicityNot monotonic
2024-03-30T02:52:39.906604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 63472
 
10.9%
74 60319
 
10.4%
33 51187
 
8.8%
22 33161
 
5.7%
34 30908
 
5.3%
41 30388
 
5.2%
82 26047
 
4.5%
36 23876
 
4.1%
38 20417
 
3.5%
85 17787
 
3.1%
Other values (42) 222396
38.3%
ValueCountFrequency (%)
1 3049
 
0.5%
2 11251
1.9%
3 3314
 
0.6%
4 449
 
0.1%
5 105
 
< 0.1%
11 1858
 
0.3%
12 1238
 
0.2%
13 12752
2.2%
14 573
 
0.1%
15 1285
 
0.2%
ValueCountFrequency (%)
93 15965
 
2.8%
92 6673
 
1.2%
91 63472
10.9%
88 576
 
0.1%
87 9609
 
1.7%
86 2257
 
0.4%
85 17787
 
3.1%
84 1897
 
0.3%
83 2248
 
0.4%
82 26047
4.5%

CRS_DEP_TIME
Real number (ℝ)

Distinct1217
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1331.1444
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:40.297298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1906
median1320
Q31743
95-th percentile2135
Maximum2359
Range2358
Interquartile range (IQR)837

Descriptive statistics

Standard deviation497.72232
Coefficient of variation (CV)0.37390559
Kurtosis-1.0834288
Mean1331.1444
Median Absolute Deviation (MAD)418
Skewness0.088311117
Sum7.7200782 × 108
Variance247727.51
MonotonicityNot monotonic
2024-03-30T02:52:40.670908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12356
 
2.1%
700 9499
 
1.6%
800 5837
 
1.0%
630 3982
 
0.7%
900 3604
 
0.6%
1000 3547
 
0.6%
830 3375
 
0.6%
730 3292
 
0.6%
1100 3288
 
0.6%
615 3118
 
0.5%
Other values (1207) 528060
91.1%
ValueCountFrequency (%)
1 17
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
13 4
 
< 0.1%
14 21
< 0.1%
ValueCountFrequency (%)
2359 835
0.1%
2358 29
 
< 0.1%
2357 33
 
< 0.1%
2356 38
 
< 0.1%
2355 261
 
< 0.1%
2354 67
 
< 0.1%
2353 63
 
< 0.1%
2352 1
 
< 0.1%
2351 29
 
< 0.1%
2350 230
 
< 0.1%

DEP_TIME
Real number (ℝ)

Distinct1398
Distinct (%)0.2%
Missing3186
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1333.2283
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:41.026923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile558
Q1907
median1324
Q31750
95-th percentile2148
Maximum2400
Range2399
Interquartile range (IQR)843

Descriptive statistics

Standard deviation512.42953
Coefficient of variation (CV)0.38435242
Kurtosis-1.0203534
Mean1333.2283
Median Absolute Deviation (MAD)422
Skewness0.04735793
Sum7.6896875 × 108
Variance262584.02
MonotonicityNot monotonic
2024-03-30T02:52:41.346776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1617
 
0.3%
557 1503
 
0.3%
556 1467
 
0.3%
558 1357
 
0.2%
554 1335
 
0.2%
655 1305
 
0.2%
559 1244
 
0.2%
553 1134
 
0.2%
657 1126
 
0.2%
654 1117
 
0.2%
Other values (1388) 563567
97.2%
(Missing) 3186
 
0.5%
ValueCountFrequency (%)
1 77
< 0.1%
2 54
< 0.1%
3 60
< 0.1%
4 52
< 0.1%
5 63
< 0.1%
6 53
< 0.1%
7 40
< 0.1%
8 58
< 0.1%
9 46
< 0.1%
10 51
< 0.1%
ValueCountFrequency (%)
2400 56
< 0.1%
2359 106
< 0.1%
2358 87
< 0.1%
2357 87
< 0.1%
2356 101
< 0.1%
2355 125
< 0.1%
2354 138
< 0.1%
2353 138
< 0.1%
2352 105
< 0.1%
2351 128
< 0.1%

DEP_DELAY
Real number (ℝ)

ZEROS 

Distinct1066
Distinct (%)0.2%
Missing3188
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean10.032191
Minimum-59
Maximum3238
Zeros28184
Zeros (%)4.9%
Negative339694
Negative (%)58.6%
Memory size4.4 MiB
2024-03-30T02:52:41.653056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile-10
Q1-5
median-2
Q37
95-th percentile67
Maximum3238
Range3297
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.40699
Coefficient of variation (CV)4.9248452
Kurtosis347.43824
Mean10.032191
Median Absolute Deviation (MAD)5
Skewness13.279506
Sum5786267
Variance2441.0506
MonotonicityNot monotonic
2024-03-30T02:52:41.984312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 45300
 
7.8%
-4 42291
 
7.3%
-3 40862
 
7.0%
-2 37138
 
6.4%
-6 35800
 
6.2%
-1 32727
 
5.6%
-7 30101
 
5.2%
0 28184
 
4.9%
-8 23333
 
4.0%
-9 17125
 
3.0%
Other values (1056) 243909
42.1%
ValueCountFrequency (%)
-59 1
 
< 0.1%
-55 1
 
< 0.1%
-42 1
 
< 0.1%
-40 1
 
< 0.1%
-39 1
 
< 0.1%
-35 1
 
< 0.1%
-34 3
< 0.1%
-33 1
 
< 0.1%
-32 2
 
< 0.1%
-31 6
< 0.1%
ValueCountFrequency (%)
3238 1
< 0.1%
3221 1
< 0.1%
2895 1
< 0.1%
2884 1
< 0.1%
2682 1
< 0.1%
2499 1
< 0.1%
2233 1
< 0.1%
2065 1
< 0.1%
1995 1
< 0.1%
1804 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct156
Distinct (%)< 0.1%
Missing3278
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.781609
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:42.463977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q319
95-th percentile31
Maximum175
Range174
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.1883753
Coefficient of variation (CV)0.48793745
Kurtosis20.360387
Mean16.781609
Median Absolute Deviation (MAD)4
Skewness3.0645717
Sum9677618
Variance67.049491
MonotonicityNot monotonic
2024-03-30T02:52:42.811738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 48654
 
8.4%
13 48325
 
8.3%
11 45668
 
7.9%
14 44668
 
7.7%
15 41031
 
7.1%
10 37393
 
6.4%
16 35543
 
6.1%
17 31045
 
5.4%
18 26298
 
4.5%
9 26090
 
4.5%
Other values (146) 191965
33.1%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 13
 
< 0.1%
3 88
 
< 0.1%
4 238
 
< 0.1%
5 639
 
0.1%
6 2441
 
0.4%
7 7184
 
1.2%
8 15289
2.6%
9 26090
4.5%
10 37393
6.4%
ValueCountFrequency (%)
175 1
< 0.1%
173 1
< 0.1%
171 2
< 0.1%
167 1
< 0.1%
159 1
< 0.1%
158 1
< 0.1%
157 1
< 0.1%
156 1
< 0.1%
155 2
< 0.1%
154 1
< 0.1%

TAXI_IN
Real number (ℝ)

Distinct146
Distinct (%)< 0.1%
Missing3387
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean8.0212203
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:43.161633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile19
Maximum168
Range167
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.5468197
Coefficient of variation (CV)0.8161875
Kurtosis41.222953
Mean8.0212203
Median Absolute Deviation (MAD)2
Skewness4.5675662
Sum4624803
Variance42.860848
MonotonicityNot monotonic
2024-03-30T02:52:43.486101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 87117
15.0%
5 81748
14.1%
6 67404
11.6%
7 55193
9.5%
3 53859
9.3%
8 42259
7.3%
9 33164
 
5.7%
10 25719
 
4.4%
11 19873
 
3.4%
12 15907
 
2.7%
Other values (136) 94328
16.3%
ValueCountFrequency (%)
1 963
 
0.2%
2 14530
 
2.5%
3 53859
9.3%
4 87117
15.0%
5 81748
14.1%
6 67404
11.6%
7 55193
9.5%
8 42259
7.3%
9 33164
 
5.7%
10 25719
 
4.4%
ValueCountFrequency (%)
168 2
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
158 1
< 0.1%
152 1
< 0.1%
151 1
< 0.1%
149 1
< 0.1%
145 1
< 0.1%
141 1
< 0.1%
139 2
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1299
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1485.5473
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:43.821167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile715
Q11056
median1513
Q31927
95-th percentile2301
Maximum2359
Range2358
Interquartile range (IQR)871

Descriptive statistics

Standard deviation527.21622
Coefficient of variation (CV)0.35489697
Kurtosis-0.51553169
Mean1485.5473
Median Absolute Deviation (MAD)417
Skewness-0.27911186
Sum8.6155503 × 108
Variance277956.94
MonotonicityNot monotonic
2024-03-30T02:52:44.194928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3343
 
0.6%
2100 2183
 
0.4%
1855 1886
 
0.3%
1810 1877
 
0.3%
2200 1855
 
0.3%
900 1748
 
0.3%
2000 1742
 
0.3%
1100 1667
 
0.3%
1940 1658
 
0.3%
1845 1643
 
0.3%
Other values (1289) 560356
96.6%
ValueCountFrequency (%)
1 33
 
< 0.1%
2 7
 
< 0.1%
3 26
 
< 0.1%
4 89
 
< 0.1%
5 749
0.1%
6 103
 
< 0.1%
7 136
 
< 0.1%
8 108
 
< 0.1%
9 107
 
< 0.1%
10 421
0.1%
ValueCountFrequency (%)
2359 3343
0.6%
2358 773
 
0.1%
2357 1185
 
0.2%
2356 519
 
0.1%
2355 1124
 
0.2%
2354 426
 
0.1%
2353 439
 
0.1%
2352 433
 
0.1%
2351 405
 
0.1%
2350 945
 
0.2%

ARR_TIME
Real number (ℝ)

Distinct1440
Distinct (%)0.2%
Missing3387
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1454.0404
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:44.511523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile629
Q11035
median1456
Q31918
95-th percentile2255
Maximum2400
Range2399
Interquartile range (IQR)883

Descriptive statistics

Standard deviation551.37251
Coefficient of variation (CV)0.37920026
Kurtosis-0.42530542
Mean1454.0404
Median Absolute Deviation (MAD)440
Skewness-0.35410675
Sum8.3835754 × 108
Variance304011.64
MonotonicityNot monotonic
2024-03-30T02:52:44.844881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1849 656
 
0.1%
1845 650
 
0.1%
1638 637
 
0.1%
1000 634
 
0.1%
1142 633
 
0.1%
1646 629
 
0.1%
2137 623
 
0.1%
927 622
 
0.1%
1150 622
 
0.1%
1633 620
 
0.1%
Other values (1430) 570245
98.3%
(Missing) 3387
 
0.6%
ValueCountFrequency (%)
1 382
0.1%
2 360
0.1%
3 346
0.1%
4 355
0.1%
5 331
0.1%
6 333
0.1%
7 312
0.1%
8 316
0.1%
9 304
0.1%
10 330
0.1%
ValueCountFrequency (%)
2400 313
0.1%
2359 384
0.1%
2358 425
0.1%
2357 434
0.1%
2356 401
0.1%
2355 424
0.1%
2354 400
0.1%
2353 457
0.1%
2352 466
0.1%
2351 441
0.1%

ARR_DELAY
Real number (ℝ)

ZEROS 

Distinct1099
Distinct (%)0.2%
Missing4529
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.8132541
Minimum-84
Maximum3241
Zeros10858
Zeros (%)1.9%
Negative369928
Negative (%)63.8%
Memory size4.4 MiB
2024-03-30T02:52:45.179192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-84
5-th percentile-27
Q1-15
median-7
Q37
95-th percentile65
Maximum3241
Range3325
Interquartile range (IQR)22

Descriptive statistics

Standard deviation51.106806
Coefficient of variation (CV)13.402413
Kurtosis307.21373
Mean3.8132541
Median Absolute Deviation (MAD)10
Skewness12.082205
Sum2194257
Variance2611.9056
MonotonicityNot monotonic
2024-03-30T02:52:45.503507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 17424
 
3.0%
-10 17140
 
3.0%
-12 17136
 
3.0%
-9 16873
 
2.9%
-13 16736
 
2.9%
-8 16585
 
2.9%
-14 16357
 
2.8%
-7 15756
 
2.7%
-15 15385
 
2.7%
-6 15351
 
2.6%
Other values (1089) 410686
70.8%
ValueCountFrequency (%)
-84 1
 
< 0.1%
-82 1
 
< 0.1%
-80 1
 
< 0.1%
-79 2
 
< 0.1%
-77 1
 
< 0.1%
-76 3
< 0.1%
-75 2
 
< 0.1%
-74 2
 
< 0.1%
-73 5
< 0.1%
-72 2
 
< 0.1%
ValueCountFrequency (%)
3241 1
< 0.1%
3237 1
< 0.1%
2900 2
< 0.1%
2682 1
< 0.1%
2499 1
< 0.1%
2260 1
< 0.1%
2072 1
< 0.1%
2006 1
< 0.1%
1832 1
< 0.1%
1790 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
576648 
1.0
 
3310

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1739874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 576648
99.4%
1.0 3310
 
0.6%

Length

2024-03-30T02:52:45.797615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:52:46.031106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 576648
99.4%
1.0 3310
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1156606
66.5%
. 579958
33.3%
1 3310
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1156606
66.5%
. 579958
33.3%
1 3310
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1156606
66.5%
. 579958
33.3%
1 3310
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1156606
66.5%
. 579958
33.3%
1 3310
 
0.2%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing576648
Missing (%)99.4%
Memory size4.4 MiB
A
1587 
B
1527 
C
192 
D
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3310
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 1587
 
0.3%
B 1527
 
0.3%
C 192
 
< 0.1%
D 4
 
< 0.1%
(Missing) 576648
99.4%

Length

2024-03-30T02:52:46.278843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:52:46.507652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
a 1587
47.9%
b 1527
46.1%
c 192
 
5.8%
d 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 1587
47.9%
B 1527
46.1%
C 192
 
5.8%
D 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1587
47.9%
B 1527
46.1%
C 192
 
5.8%
D 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1587
47.9%
B 1527
46.1%
C 192
 
5.8%
D 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1587
47.9%
B 1527
46.1%
C 192
 
5.8%
D 4
 
0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
578740 
1.0
 
1218

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1739874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 578740
99.8%
1.0 1218
 
0.2%

Length

2024-03-30T02:52:46.746457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:52:46.958196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 578740
99.8%
1.0 1218
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1158698
66.6%
. 579958
33.3%
1 1218
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1158698
66.6%
. 579958
33.3%
1 1218
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1158698
66.6%
. 579958
33.3%
1 1218
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1739874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1158698
66.6%
. 579958
33.3%
1 1218
 
0.1%

AIR_TIME
Real number (ℝ)

Distinct608
Distinct (%)0.1%
Missing4529
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean113.73129
Minimum8
Maximum648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:47.339863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q162
median96
Q3143
95-th percentile272
Maximum648
Range640
Interquartile range (IQR)81

Descriptive statistics

Standard deviation70.123149
Coefficient of variation (CV)0.61656863
Kurtosis2.3084628
Mean113.73129
Median Absolute Deviation (MAD)38
Skewness1.402298
Sum65444285
Variance4917.256
MonotonicityNot monotonic
2024-03-30T02:52:47.658696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 5102
 
0.9%
65 5101
 
0.9%
64 5065
 
0.9%
62 4933
 
0.9%
61 4874
 
0.8%
66 4847
 
0.8%
67 4810
 
0.8%
53 4791
 
0.8%
54 4770
 
0.8%
60 4769
 
0.8%
Other values (598) 526367
90.8%
ValueCountFrequency (%)
8 5
 
< 0.1%
9 19
 
< 0.1%
10 25
 
< 0.1%
11 15
 
< 0.1%
12 8
 
< 0.1%
13 12
 
< 0.1%
14 11
 
< 0.1%
15 31
 
< 0.1%
16 104
< 0.1%
17 190
< 0.1%
ValueCountFrequency (%)
648 1
< 0.1%
646 1
< 0.1%
645 1
< 0.1%
639 1
< 0.1%
636 1
< 0.1%
632 1
< 0.1%
631 1
< 0.1%
630 1
< 0.1%
625 2
< 0.1%
624 1
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct844
Distinct (%)0.8%
Missing475839
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean25.089263
Minimum0
Maximum3221
Zeros41858
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:48.030261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q323
95-th percentile101
Maximum3221
Range3221
Interquartile range (IQR)23

Descriptive statistics

Standard deviation73.84996
Coefficient of variation (CV)2.9434886
Kurtosis207.27696
Mean25.089263
Median Absolute Deviation (MAD)6
Skewness11.057327
Sum2612269
Variance5453.8167
MonotonicityNot monotonic
2024-03-30T02:52:48.368164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41858
 
7.2%
2 2085
 
0.4%
1 2055
 
0.4%
6 1951
 
0.3%
3 1933
 
0.3%
15 1919
 
0.3%
4 1896
 
0.3%
7 1844
 
0.3%
5 1828
 
0.3%
16 1752
 
0.3%
Other values (834) 44998
 
7.8%
(Missing) 475839
82.0%
ValueCountFrequency (%)
0 41858
7.2%
1 2055
 
0.4%
2 2085
 
0.4%
3 1933
 
0.3%
4 1896
 
0.3%
5 1828
 
0.3%
6 1951
 
0.3%
7 1844
 
0.3%
8 1719
 
0.3%
9 1653
 
0.3%
ValueCountFrequency (%)
3221 1
< 0.1%
2884 1
< 0.1%
2682 1
< 0.1%
2499 1
< 0.1%
2233 1
< 0.1%
1804 1
< 0.1%
1782 1
< 0.1%
1766 1
< 0.1%
1754 1
< 0.1%
1664 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct349
Distinct (%)0.3%
Missing475839
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean2.8303672
Minimum0
Maximum1439
Zeros99571
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:48.681896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1439
Range1439
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.373939
Coefficient of variation (CV)8.611582
Kurtosis745.43133
Mean2.8303672
Median Absolute Deviation (MAD)0
Skewness22.112416
Sum294695
Variance594.0889
MonotonicityNot monotonic
2024-03-30T02:52:49.015685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99571
 
17.2%
17 86
 
< 0.1%
6 81
 
< 0.1%
19 81
 
< 0.1%
16 81
 
< 0.1%
5 74
 
< 0.1%
18 73
 
< 0.1%
13 72
 
< 0.1%
8 69
 
< 0.1%
15 69
 
< 0.1%
Other values (339) 3862
 
0.7%
(Missing) 475839
82.0%
ValueCountFrequency (%)
0 99571
17.2%
1 60
 
< 0.1%
2 53
 
< 0.1%
3 57
 
< 0.1%
4 46
 
< 0.1%
5 74
 
< 0.1%
6 81
 
< 0.1%
7 62
 
< 0.1%
8 69
 
< 0.1%
9 66
 
< 0.1%
ValueCountFrequency (%)
1439 1
< 0.1%
1393 1
< 0.1%
1188 1
< 0.1%
942 1
< 0.1%
937 1
< 0.1%
934 1
< 0.1%
927 1
< 0.1%
920 1
< 0.1%
919 1
< 0.1%
905 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct330
Distinct (%)0.3%
Missing475839
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean10.469444
Minimum0
Maximum1515
Zeros57455
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:49.348367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile45
Maximum1515
Range1515
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.456887
Coefficient of variation (CV)2.5270576
Kurtosis387.26735
Mean10.469444
Median Absolute Deviation (MAD)0
Skewness12.826572
Sum1090068
Variance699.96689
MonotonicityNot monotonic
2024-03-30T02:52:49.676328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57455
 
9.9%
1 2686
 
0.5%
2 1980
 
0.3%
15 1894
 
0.3%
3 1845
 
0.3%
4 1810
 
0.3%
16 1713
 
0.3%
5 1614
 
0.3%
6 1576
 
0.3%
17 1534
 
0.3%
Other values (320) 30012
 
5.2%
(Missing) 475839
82.0%
ValueCountFrequency (%)
0 57455
9.9%
1 2686
 
0.5%
2 1980
 
0.3%
3 1845
 
0.3%
4 1810
 
0.3%
5 1614
 
0.3%
6 1576
 
0.3%
7 1487
 
0.3%
8 1373
 
0.2%
9 1291
 
0.2%
ValueCountFrequency (%)
1515 1
< 0.1%
1421 1
< 0.1%
1065 1
< 0.1%
975 1
< 0.1%
905 1
< 0.1%
883 1
< 0.1%
857 1
< 0.1%
831 1
< 0.1%
811 1
< 0.1%
800 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct95
Distinct (%)0.1%
Missing475839
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean0.16002843
Minimum0
Maximum1183
Zeros103517
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:50.042733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1183
Range1183
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8834056
Coefficient of variation (CV)30.515863
Kurtosis33823.228
Mean0.16002843
Median Absolute Deviation (MAD)0
Skewness151.31394
Sum16662
Variance23.84765
MonotonicityNot monotonic
2024-03-30T02:52:50.408292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103517
 
17.8%
10 30
 
< 0.1%
15 26
 
< 0.1%
17 25
 
< 0.1%
8 24
 
< 0.1%
12 23
 
< 0.1%
13 21
 
< 0.1%
16 20
 
< 0.1%
18 19
 
< 0.1%
19 18
 
< 0.1%
Other values (85) 396
 
0.1%
(Missing) 475839
82.0%
ValueCountFrequency (%)
0 103517
17.8%
1 13
 
< 0.1%
2 14
 
< 0.1%
3 17
 
< 0.1%
4 13
 
< 0.1%
5 14
 
< 0.1%
6 15
 
< 0.1%
7 17
 
< 0.1%
8 24
 
< 0.1%
9 15
 
< 0.1%
ValueCountFrequency (%)
1183 1
< 0.1%
371 1
< 0.1%
289 1
< 0.1%
277 1
< 0.1%
272 1
< 0.1%
223 1
< 0.1%
196 1
< 0.1%
146 1
< 0.1%
137 2
< 0.1%
133 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct664
Distinct (%)0.6%
Missing475839
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean26.393655
Minimum0
Maximum3228
Zeros50580
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T02:52:50.714500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q330
95-th percentile115
Maximum3228
Range3228
Interquartile range (IQR)30

Descriptive statistics

Standard deviation61.088421
Coefficient of variation (CV)2.3145116
Kurtosis187.81024
Mean26.393655
Median Absolute Deviation (MAD)3
Skewness9.1429102
Sum2748081
Variance3731.7952
MonotonicityNot monotonic
2024-03-30T02:52:51.020593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50580
 
8.7%
15 1361
 
0.2%
16 1332
 
0.2%
17 1260
 
0.2%
18 1174
 
0.2%
19 1103
 
0.2%
20 1096
 
0.2%
21 1024
 
0.2%
14 1001
 
0.2%
11 989
 
0.2%
Other values (654) 43199
 
7.4%
(Missing) 475839
82.0%
ValueCountFrequency (%)
0 50580
8.7%
1 711
 
0.1%
2 738
 
0.1%
3 720
 
0.1%
4 778
 
0.1%
5 779
 
0.1%
6 888
 
0.2%
7 850
 
0.1%
8 908
 
0.2%
9 868
 
0.1%
ValueCountFrequency (%)
3228 1
< 0.1%
2065 1
< 0.1%
1970 1
< 0.1%
1664 1
< 0.1%
1557 1
< 0.1%
1525 1
< 0.1%
1434 1
< 0.1%
1421 1
< 0.1%
1380 1
< 0.1%
1353 1
< 0.1%

Interactions

2024-03-30T02:52:11.122269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:06.088047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:13.253309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:19.150307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:26.008817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:33.040147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:39.694973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:45.596064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:52.256054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:58.881411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:06.454638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:13.254080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:20.293870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:26.784993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:33.680857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:40.827866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:47.653242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:53.262788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:58.828089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:05.384124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:11.412679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:06.803555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:13.552991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:19.492091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:26.387846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:33.395900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:40.051048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:45.941006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:52.588066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:59.205099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:06.803584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:13.588207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:20.655274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:27.130475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:34.080973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:41.172992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:47.999260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:53.525054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:59.089092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:05.680781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:11.660981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:07.224950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:13.868550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:19.769516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:26.720212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:33.730731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:40.387378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:46.293762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:52.919843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:59.541590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:07.135637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:13.913149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:20.993580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:27.452682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:34.505292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:41.670492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:48.285560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:53.786333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:59.336586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:05.970076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:11.989189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:07.603002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:14.255093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:20.158014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:27.066399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:34.143074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:40.838596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:46.651627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:53.300335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:59.926821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:07.485961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:14.344315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:21.341160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:27.790943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:34.896172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:42.062386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:48.573396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:54.093582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:59.622641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:06.339097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:12.272149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:07.919166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:14.557893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:20.421247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:27.419293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:34.478284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:41.130115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:46.969704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:53.606616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:00.386772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:07.822954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:14.673133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:21.707492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:28.146922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:35.290101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:42.418821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:48.817380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:54.385185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:59.875939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:06.759946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:12.545734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:08.350559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:14.855125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:20.752035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:27.867989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:34.857602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:41.396492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:47.311632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:53.975170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:00.780302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:08.232161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:15.030761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:22.082339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:28.498814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:35.690358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:42.762319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:49.110279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:54.652342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:00.229733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:07.066891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:12.800718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:08.687603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:15.173645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:21.207485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:28.487084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:35.195955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:41.658898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:47.601580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:54.340334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:01.312423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:08.561560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:15.391748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:22.419000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:28.813704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:36.099538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:43.089413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:49.341474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:54.917134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:00.485550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:07.307672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:13.059267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:09.024692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:15.472593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:21.525611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:28.790543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:35.519145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:41.922979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:47.890388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:54.640718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:01.683936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:08.899321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:15.732958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:22.734565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:29.156688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:36.482114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:43.377372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:49.597067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:55.174702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:00.752923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:07.574108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:13.322639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:09.359175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:15.754932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:21.837186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:29.129969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:35.839913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:50:48.251501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:54.990793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:02.048970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:09.245268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:16.123177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:51:19.474159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:26.172525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:32.982688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:40.172003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:47.060568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:52.721840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:58.272050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:04.768753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:10.596364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:16.372015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:12.904294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:18.814138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:25.566945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:32.671370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:39.331263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:45.244362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:51.828090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:50:58.505752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:05.806167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:12.840465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:19.738416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:26.417067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:33.278242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:40.449592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:47.367323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:52.986498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:51:58.564568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:05.078325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:52:10.858225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:52:17.070729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:52:20.002081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
015/1/2023 12:00:00 AM9E462814576ROCRochester, NYNew York2212953LGANew York, NYNew York221000955.0-5.012.033.011221128.06.00.0NaN0.048.0NaNNaNNaNNaNNaN
115/1/2023 12:00:00 AM9E462912397ITHIthaca/Cortland, NYNew York2212478JFKNew York, NYNew York2215201605.045.013.09.016291712.043.00.0NaN0.045.00.00.00.00.043.0
215/1/2023 12:00:00 AM9E463011042CLECleveland, OHOhio4412478JFKNew York, NYNew York2216441642.0-2.010.08.018291811.0-18.00.0NaN0.071.0NaNNaNNaNNaNNaN
315/1/2023 12:00:00 AM9E463112264IADWashington, DCVirginia3812953LGANew York, NYNew York2218051849.044.014.07.019291958.029.00.0NaN0.048.00.00.00.00.029.0
415/1/2023 12:00:00 AM9E463212478JFKNew York, NYNew York2211042CLECleveland, OHOhio4414581453.0-5.028.010.017041651.0-13.00.0NaN0.080.0NaNNaNNaNNaNNaN
515/1/2023 12:00:00 AM9E463311042CLECleveland, OHOhio4412953LGANew York, NYNew York2217501747.0-3.018.06.019251924.0-1.00.0NaN0.073.0NaNNaNNaNNaNNaN
615/1/2023 12:00:00 AM9E463412953LGANew York, NYNew York2211042CLECleveland, OHOhio4412501259.09.024.017.014411447.06.00.0NaN0.067.0NaNNaNNaNNaNNaN
715/1/2023 12:00:00 AM9E463512478JFKNew York, NYNew York2210821BWIBaltimore, MDMaryland3514551453.0-2.025.06.016251601.0-24.00.0NaN0.037.0NaNNaNNaNNaNNaN
815/1/2023 12:00:00 AM9E463610423AUSAustin, TXTexas7414492RDURaleigh/Durham, NCNorth Carolina361000950.0-10.011.03.013521318.0-34.00.0NaN0.0134.0NaNNaNNaNNaNNaN
915/1/2023 12:00:00 AM9E463812953LGANew York, NYNew York2213244MEMMemphis, TNTennessee548591132.0153.029.06.011021326.0144.00.0NaN0.0139.0144.00.00.00.00.0
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
57994875/28/2023 12:00:00 AMYX584411278DCAWashington, DCVirginia3812953LGANew York, NYNew York2218001754.0-6.08.07.019401854.0-46.00.0NaN0.045.0NaNNaNNaNNaNNaN
57994975/28/2023 12:00:00 AMYX584610721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3818251822.0-3.019.02.020201951.0-29.00.0NaN0.068.0NaNNaNNaNNaNNaN
57995075/28/2023 12:00:00 AMYX584810721BOSBoston, MAMassachusetts1312478JFKNew York, NYNew York22906902.0-4.018.07.010361009.0-27.00.0NaN0.042.0NaNNaNNaNNaNNaN
57995175/28/2023 12:00:00 AMYX585210721BOSBoston, MAMassachusetts1314100PHLPhiladelphia, PAPennsylvania2319251915.0-10.017.05.021062028.0-38.00.0NaN0.051.0NaNNaNNaNNaNNaN
57995275/28/2023 12:00:00 AMYX585312478JFKNew York, NYNew York2210721BOSBoston, MAMassachusetts13600558.0-2.012.07.0720700.0-20.00.0NaN0.043.0NaNNaNNaNNaNNaN
57995375/28/2023 12:00:00 AMYX585410693BNANashville, TNTennessee5412953LGANew York, NYNew York2212531245.0-8.016.010.016211619.0-2.00.0NaN0.0128.0NaNNaNNaNNaNNaN
57995475/28/2023 12:00:00 AMYX585412953LGANew York, NYNew York2210693BNANashville, TNTennessee5410241015.0-9.013.03.012081114.0-54.00.0NaN0.0103.0NaNNaNNaNNaNNaN
57995575/28/2023 12:00:00 AMYX585514730SDFLouisville, KYKentucky5212953LGANew York, NYNew York2212361235.0-1.014.07.014521445.0-7.00.0NaN0.0109.0NaNNaNNaNNaNNaN
57995675/28/2023 12:00:00 AMYX585714524RICRichmond, VAVirginia3812953LGANew York, NYNew York2219422119.097.012.06.021122228.076.00.0NaN0.051.076.00.00.00.00.0
57995775/28/2023 12:00:00 AMYX586110721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3810191012.0-7.016.06.012041138.0-26.00.0NaN0.064.0NaNNaNNaNNaNNaN